Yearly Routine Immunization (RI) dummy data

A data frame containing dummy data for Routine Immunization (RI) across fakeland country's administrative units yearly for the period 2010 to 2020.

Usage

dummy_yearly_ri_data

Format

A data frame with 440 rows and 8 columns:

prov_code, dist_code: [character] As defined in dummy_monthly_ri_data.
year: [integer] As defined in dummy_monthly_ri_data.
OPV0, OPV1, IPV1, IPV2: [numeric] As defined in dummy_monthly_ri_data but per year.
live_births: [numeric] As defined in dummy_monthly_ri_data but per year.

Source

Generated data for package examples

Details

Important notes

Zero doses should be explicitly recorded as 0 (not missing)
Missing values (NA) are allowed only for dose columns

When to use Yearly vs Monthly data:

Yearly: When monthly variation is not needed or data not available
Monthly: For seasonality analysis or when birth seasonality is important

Examples

# \donttest{
  # View the data
  head(dummy_yearly_ri_data)
#>   prov_code dist_code year  OPV0  OPV1  IPV1  IPV2 live_births
#> 1      PR_A       A01 2010 42834 37177 40987    NA       42862
#> 2      PR_A       A01 2011 44935 38919    NA 40944       41429
#> 3      PR_A       A01 2012 43244 44548 38286    NA       43461
#> 4      PR_A       A01 2013 38402 36718 44257 47729       43097
#> 5      PR_A       A01 2014 41949 37485 44051 47834       42742
#> 6      PR_A       A01 2015 45877 45184    NA 35260       43190

  # Validate the data
  ## Set admin to tell the package to expect the corresponding admin unit columns in
  ## the data for validation
  with_config(list(admin = c('prov_code', 'dist_code')), {
    validate_ri_data(dummy_yearly_ri_data, prep_dummy_vs_info)
  })

  # Check dose columns present
  grep("^(OPV|IPV)", names(dummy_yearly_ri_data), value = TRUE)
#> [1] "OPV0" "OPV1" "IPV1" "IPV2"
# }